Quaternion-based robust extended Kalman filter for attitude estimation of micro quadrotors using low-cost MEMS

2016 ◽  
Author(s):  
Xiaoran Li ◽  
Mou Chen ◽  
Lei Zhang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Attitude Estimation

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

scholarly journals Experimentally Validated Extended Kalman Filter for UAV State Estimation Using Low-Cost Sensors

2018 ◽  
Vol 51 (15) ◽  
pp. 43-48 ◽  
Author(s):  
S.P.H. Driessen ◽  
N.H.J. Janssen ◽  
L. Wang ◽  
J.L. Palmer ◽  
H. Nijmeijer
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Low Cost

Design and Implementation of Sensing and Estimation Software in AggieNav, a Small UAV Navigation Platform

2009 ◽  
Author(s):  
Calvin Coopmans ◽  
Haiyang Chao ◽  
YangQuan Chen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
High Accuracy ◽  
Level System ◽  
Design And Implementation ◽  
Small Uav ◽  
High Level ◽  
Navigational Systems ◽  
Uav Navigation

Small UAV performance is limited by the sensors and software filters used in the navigational systems. Several solutions of various complexity and cost exist, however no ready-made solutions exist for a high-accuracy, low-cost UAV system. Presented is the design (low-level system as well as high-level extended Kalman filter) for a specifically designed small-UAV navigation platform, AggieNav.

Extended Kalman Filter Based In-Cylinder Temperature Estimation for Diesel Engines With Thermocouple Lag Compensation

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Song Chen ◽  
Fengjun Yan
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Diesel Engines ◽  
Measurement Errors ◽  
Low Cost ◽  
Estimation Method ◽  
Measured Temperature ◽  
Temperature Estimation ◽  
Engine Model ◽  
Auto Ignition

The in-cylinder temperature information is critical for auto-ignition combustion control in diesel engines, but difficult to be directly accessed at low cost in production engines. Through investigating the thermodynamics of Tivc, cycle-by-cycle models are proposed in this paper for the estimation of in-cylinder temperature at the crank angle of intake valve closing (IVC), referred to as Tivc. An extended Kalman filter (EKF) based method was devised by utilizing the measurable temperature information from the intake and exhaust manifolds. Due to the fact that measured temperature signals by typical thermocouples have slow responses which can be modeled as first-order lags with varying time-constants, temperature signals need to be reconstructed in transient conditions. In the proposed EKF estimation method, this issue can be effectively addressed by analyzing the measurement errors and properly selecting the noises covariance matrices. The proposed estimation method was validated through a high-fidelity GT-power engine model.

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